Towards Seed-Free Music Playlist Generation: Enhancing Collaborative Filtering with Playlist Title Information

1. Towards Seed-Free Music Playlist Generation: Enhancing Collaborative Filtering with Playlist Title Information Jaehun Kim, Minz Won, Cynthia C. S. Liem, Alan Hanjalic 1

2. Preliminary Approach

3. First attempt : WRMF 3 ● Good Old MF ○ Weighted Regularized Matrix Factorization [1] ■ Developed for implicit feedback ■ ALS* optimization : fast and reliable ■ Only 2~3 hyper parameters *Alternating Least Square (or Coordinate Descent) R U~= x V

4. First attempt : WRMF 4

5. First attempt : WRMF 5

6. First attempt : WRMF 6 NO SEED Average Winner

7. First attempt : WRMF 7 ● Good Old MF ○ Already reasonable performance ○ Except No-Seed case => Cold Start Problem ● Any metadata or content for playlist?

8. First attempt : WRMF 8 ● Good Old MF ○ Already reasonable performance ○ Except No-Seed case => Cold Start Problem ● Any metadata or content for playlist? ○ Playlist titles!

9. Playlist Titles

10. ● Text information (implicitly) represents the playlist ● Some key statistics Playlist Titles 10 # of titles (MPD + Challenge Set) 1,010,000 # of unique titles 93,250 # of unique titles (stemmed) 49,808 Single word ~60% Less than two words ~92%

11. ● Text information (implicitly) represents the playlist ● Some key statistics Playlist Titles 11 # of titles (MPD + Challenge Set) 1,010,000 # of unique titles 93,250 # of unique titles (stemmed) 49,808 Single word ~60% Less than two words ~92% 1. Playlist titles ~= Words

12. ● Noisiness Playlist Titles 12 Categories Examples Special characters //Pretty Little Liars//, ** some tunes, ?!? Repeated characters Yaaaas, summerrrr, partayyy Shortened words Chillin, Temp, favss Abbreviated words loml, jb, IDFK, jjjj Symbolic expressions Multiple languages otoño, 電台收藏, アニメ

13. ● Noisiness Playlist Titles 13 Categories Examples Special characters //Pretty Little Liars//, ** some tunes, ?!? Repeated characters Yaaaas, summerrrr, partayyy Shortened words Chillin, Temp, favss Abbreviated words loml, jb, IDFK, jjjj Symbolic expressions Multiple languages otoño, 電台收藏, アニメ 2. Standard word-level approaches (may be) NOT WORKING

14. Playlist Titles 14 ● Playlist titles ~= words ● Standard word-level approaches (may be) not working ● Character level approach : Character N-GRAM

15. Character N-gram 15 Input Text “Character N-gram” Unigram (1-gram) C, h, a, r, a, c, t, e, r, , N, -, g, r, a, m Bigram (2-gram) Ch, ha, ar, ac, ct, te, er, r , N, N-, -g, ... Trigram (3-gram) Cha, har, arc, rct, cte, ter, er , r N, N-, ... Quadrogram (4-gram) Char, harc, arct, rcte, cter, ter , er N, ... ... ...

16. Character N-gram 16 Input Text “Character N-gram” Unigram (1-gram) C, h, a, r, a, c, t, e, r, , N, -, g, r, a, m Bigram (2-gram) Ch, ha, ar, ac, ct, te, er, r , N, N-, -g, ... Trigram (3-gram) Cha, har, arc, rct, cte, ter, er , r N, N-, ... Quadrogram (4-gram) Char, harc, arct, rcte, cter, ter , er N, ... ... ... Cha har arc cte ter er r N N-abc zeb ytj pyk nrc nfe 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 ... jkl Bag-of-Character-N-gram

17. ● Build bag-of-n-grams for each playlist (Train + Test Set) ● For each testing playlist ○ Find M closest playlist in Train set using cosine distance ○ Collect tracks from retrieved playlist ○ Recommend L most popular tracks Title-based RecSys NGRAM:Similarity Based 17

18. Cosine Distance Title-based RecSys NGRAM:Similarity Based 18 Cha har arc cte ter er r N N-abc zeb ytj pyk nrc nfe 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 ... jkl ... ... Testing Playlists Training Playlist L most popular tracks among M closest training playlists Bag-of-Character-N-gram

19. Title-based RecSys :: Model Based 19

20. Title-based RecSys :: Model Based 20 1. (pre-trained) Transfer`Title info’

21. Title-based RecSys :: Model Based 21 1. 2. Substitute

22. Title-based RecSys :: Model Based 22 1. 2. ?X. Wang et al. (2014) [2] A. Van den Oord et al. (2013) [3]

23. Title-based RecSys :: Model Based 23 1. 2. + ?!

24. Proposed Model

25. Multi-Objective Collab. Filtering 25 MF for Pre-trained Factors Main objective function for

26. Multi-Objective Collab. Filtering 26 1. 2.

27. Multi-Objective Collab. Filtering 27 1. 2.

28. RNCF : Recurrent Neural CF 28

29. Results

30. Results:Overall 30 ● It works! (10th on final Leaderboard)

31. Results:Overall 31 ● Superior over simple baselines

32. Results:Overall 32 ● WRMF > SVD

33. HRNCF : Hybrid RNCF 33 R U~= x V Filling missing factors

34. Results:Overall 34 ● HRNCF: best of both worlds

35. Results:Overall 35 ● Simple NGRAM distance may have given us very similar performance

36. Hyperparameters: WRMF 36

37. Hyperparameters: WRMF 37

38. Hyperparameters: RNCF 38

41. Discussion & Take away

42. Take away 42 ● MF is still powerful ● Setting up right (internal) evaluation setup is more important than model ● Software engineering DOES MATTER ○ Since the scalability DOES MATTER ○ Since hyper-parameter tuning DOES MATTER ● Deep learning is not a magic wand ○ No Free Lunch ○ It costs a LOT ● Content-based algorithms still gives small (but significant) gain to CF

43. Thank you! Code: https://github.com/eldrin/recsys18-spotify-spotif-ai 43

44. References 44 [1] Hu, Yifan, Yehuda Koren, and Chris Volinsky. "Collaborative filtering for implicit feedback datasets." Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. Ieee, 2008. [2] Wang, Xinxi, and Ye Wang. "Improving content-based and hybrid music recommendation using deep learning." Proceedings of the 22nd ACM international conference on Multimedia. ACM, 2014. [3] Van den Oord, Aäron, Sander Dieleman, and Benjamin Schrauwen. "Deep content-based music recommendation." Advances in neural information processing systems. 2013.